tf_agents.metrics.tf_metrics.NumberOfEpisodes

Counts the number of episodes in the environment.

Inherits From: TFStepMetric

tf_agents.metrics.tf_metrics.NumberOfEpisodes(
    name=&#x27;NumberOfEpisodes',
    prefix=&#x27;Metrics',
    dtype=tf.int64
)

Used in the notebooks

Used in the tutorials
Drivers

Methods

`call`

View source

call(
    trajectory
)

Increase the number of number_episodes according to trajectory.

It would increase for all trajectory.is_last().

Args
`trajectory`	A tf_agents.trajectory.Trajectory

Returns
The arguments, for easy chaining.

`init_variables`

View source

init_variables()

Initializes this Metric's variables.

Should be called after variables are created in the first execution of __call__(). If using graph execution, the return value should be run() in a session before running the op returned by __call__(). (See example above.)

Returns
If using graph execution, this returns an op to perform the initialization. Under eager execution, the variables are reset to their initial values as a side effect and this function returns None.

`reset`

View source

reset()

`result`

View source

result()

Computes and returns a final value for the metric.

`tf_summaries`

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tf_summaries(
    train_step=None, step_metrics=()
)

Generates summaries against train_step and all step_metrics.

Args
`train_step`	(Optional) Step counter for training iterations. If None, no metric is generated against the global step.
`step_metrics`	(Optional) Iterable of step metrics to generate summaries against.

Returns
A list of summaries.

`call`

View source

__call__(
    *args, **kwargs
)

Returns op to execute to update this metric for these inputs.

Returns None if eager execution is enabled. Returns a graph-mode function if graph execution is enabled.

Args

Args
`*args`
`**kwargs`	A mini-batch of inputs to the Metric, passed on to `call()`.

*args

**kwargs A mini-batch of inputs to the Metric, passed on to call().